Cek Similitary atau Originality Artikel ICITRI ASY

Cek Similitary atau Originality Artikel ICITRI ASY

• By Andi Saryoko, Elin Panca Saputra, Siti Nurajizah, Mawadatul Maulidah, Nadiyah Hidayati;
• Seminar Internasional Bereputasi
• 143

This paper presents mushroom prediction using
machine learning methods to identify poisonous and non-toxic
mushrooms and find the level of accuracy of several machine
learning algorithms. Several algorithms are tested to get the
best performance, namely by using Neural Network algorithm,
Logistic Regression, Support Vector Machine Learning, Naïve
Bayes, Decision Tree, Random Forest. The Neural Network
algorithm occupies the accuracy value with the highest
performance, with an accuracy value of 92.98%. Next, DDN is
optimizing by using RMSprop, Adam SGD, Adagrad, and
Adadelta with a learning rate comparison of 20 epochs. The
experiment show that it produces better accuracy value, so we
conduct some experiments by using Deep Neural Network
(DNN) in terms of accuracy value. Deep Neural Network with
non-transfer learning produces 99.38% accuracy with the
Adagrad optimizer for classification of mushroom plants. The
experimental results show that the classification using the Deep
Neural Network (DNN) is able to achieve higher accuracy than
other algorithms to classify mushroom plants.

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